How we assess
Every applicant goes through 5 gates: application, an AI-assisted screen, a live technical assessment scored by a practice lead, a client-fit interview, and reference and credential checks. The questions below come from the live assessment. We score answers on reasoning and trade-offs, not on recalling definitions.
20 technical questions with model-answer hints
Questions 1–10 (role-specific)
Question 1: How do you find out why a scene is GPU-bound?
Model-answer hint
GPU profiler, overdraw and shader-complexity views, resolution tests, isolating passes.
Question 2: How do you write a shader that scales across quality tiers?
Model-answer hint
feature toggles, static switches or keywords, LOD, fallbacks.
Question 3: What drives texture memory, and how do you control it?
Model-answer hint
resolution, compression formats, mips, streaming, atlases.
Question 4: How do you set an LOD strategy for environments?
Model-answer hint
screen-size thresholds, automated generation, HLODs or impostors, validation.
Question 5: Describe a pipeline tool you built and its impact.
Model-answer hint
the problem, design choices, adoption, time saved.
Question 6: How do you keep VFX within budget?
Model-answer hint
particle counts, overdraw, GPU versus CPU simulation, LOD, per-effect budgets.
Question 7: How do you validate assets on import?
Model-answer hint
automated checks for naming, polycount, texture size and materials, with reports.
Question 8: When is Houdini procedural generation worth it?
Model-answer hint
repeatable variation, large worlds, iteration speed versus setup cost.
Question 9: Which rig standards matter for performance?
Model-answer hint
bone limits, influences per vertex, LOD rigs.
Question 10: How do you explain a performance constraint to an art director?
Model-answer hint
show the cost visually, offer alternatives, agree shared budgets.
Questions 11–20 (Game Development core)
Question 11: How do you find and fix a frame-time spike on target hardware?
Model-answer hint
platform profiler, isolate CPU, GPU or I/O, per-system budgets, verify on device.
Question 12: How do you manage memory budgets on mobile?
Model-answer hint
per-category budgets, texture compression, streaming, leak tracking on low-end devices.
Question 13: How do you keep gameplay data-driven for designers?
Model-answer hint
config assets or tables, hot reload, validation, simple tooling.
Question 14: A mechanic “doesn’t feel right”. What do you do?
Model-answer hint
quick prototype, exposed tuning values, playtests, combine feel with telemetry.
Question 15: How do you organise version control for large binary assets?
Model-answer hint
Perforce streams or Git LFS, file locking, branch strategy, CI builds.
Question 16: What makes a build pipeline dependable for a live game?
Model-answer hint
automated builds, smoke tests, versioned content, staged rollout, fast rollback.
Question 17: How do you prepare a title for platform certification?
Model-answer hint
requirements checklist from day one, automated checks, an internal pre-certification pass.
Question 18: How do you use telemetry after soft launch?
Model-answer hint
funnels, D1/D7/D30 retention, cohorts, A/B tests, guardrail metrics.
Question 19: How do you handle Arabic text and RTL layouts in game UI?
Model-answer hint
glyph shaping, mirrored layouts, font fallback, text expansion, culturalisation review.
Question 20: Describe a feature you cut or rescoped to hit a milestone.
Model-answer hint
trade-off logic, stakeholder alignment, effect on the player.
Interviewer tip: Ask for a real example after every answer. Strong candidates describe what they built, what broke and what they changed.
Practical task
A hero environment scene misses its frame budget on a target mobile device. Identify the top 3 costs with profiler evidence, deliver optimised versions of one shader and one asset set, and write a short guideline to prevent a repeat.
How to run it Share the brief 24 hours ahead or run it live. Ask the candidate to talk through trade-offs as they work; reasoning matters more than a finished artefact.
Red flags our practice leads watch for
- Portfolio shows strong art with no performance awareness.
- Cannot script or automate repetitive work.
- Has never profiled on target hardware.
- Struggles to explain technical constraints to artists.
- Universal flag: Vague answers that never reach a concrete system, number or decision the candidate personally owned.
The scorecard we use
Score each dimension 1–4 (1 = no evidence, 2 = partial, 3 = solid, 4 = exceptional):
| Dimension | Weight | What a 4 looks like |
|---|---|---|
| Technical depth, production judgement and security | 40% | Explains how and why, designs for failure and sensitive data, and knows each tool’s limits |
| Practical task and problem solving | 30% | Delivers a working solution with tests and documented decisions |
| Communication and documentation | 20% | Explains clearly to technical and non-technical audiences, in English or Arabic |
| References and regional context | 10% | References confirm the claimed scope; knows the frameworks and data-residency rules that apply |
Pass bar: weighted average of 3.0 out of 4 or higher, with no dimension below 2. The practice lead approves the final result.